desarrollo de control de procesos en strata cooper

7/30/2019 Desarrollo de Control de Procesos en Strata Cooper

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Developments in Process Control

With particular reference to comminution and flotation

(McGill Professional Development Seminar Mineral Processing Systems)

by P. Thwaites, May 15th, 2009


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Xstrata OrganizationalStructure

Xstrata Executive Committee

X Ni X Cu X Coal XTS X Alloys X Zn

XPS XT

XTS : Xstrata Technology Services Thras Moraitis - LondonXPS : Xstrata Process Support Frank McGlynn - SudburyXT : Xstrata Technology Joe Pease - Brisbane


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Objectives of McGill Short Course

The objectives of the Mineral ProcessingSystems, Professional Development Seminarare:

To cover new and important developments in

mineral processing in the areas of comminution,flotation, process control, environment andoptimization.

Developments in Process Control, with particular

reference to comminution and flotation: New sensor technologies, control strategies, optimization

etc.


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Opportunities to consider

Operational Performance Excellence requires a solid performance of theregulatory layer AND process optimisation.

For Plant Operators: Is your feed stable? Are your instruments calibrated and performing? Are you aware of wireless instruments (including vibration)? Is your control system up to date and stable? Are you in manual or auto control? Are your Operators acting on alarms or are they nuisance? Do you understand and accept your process variability? Are you operating within the design targets and process constraints

(pumps, cyclones, supplies, roasters, furnaces etc.)?

Are you using your surge capacity, . or running tight level control? Are you at optimum and are the controls robust? Are you benefiting from asset management systems? Are failure / fault detection systems implemented? Can you make the same product for less energy consumption?

(P. Thwaites, AUTOMINING2008, Chile)


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Downtime Reporter(Matrikon)

Automatic downtimerecognition withdirect data fromProcess Control

System

PlantEquipment

PLC/DCS


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(Matrikon) Downtime Reporter:How do we improve OEE*?

Improve Availability

Downtime Paretoreport clearly identifiesthe big-ticket items

causing the mostdowntime in the plant.

Strategies can then be formulated that eliminatethese problems and thus improve availability.

* OEE Overall Equipment Effectiveness = Availability x Performance x Qualityi.e. 100% = 100% x 100% x 100%OEE defines the expected performance of a machine, measures it and providesa loss structure for analysis, which leads to improvement.


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Definition of Process Control

(McKee, AMIRA P9L)

Process control is a broad term which often meansdifferent things to different people.

Process control is considered as the technology

required to obtain information in real time on processbehaviour and then use that information to manipulate

process variables with the objective of improving themetallurgical performance of the plant.

Control for the purpose of process improvement.


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Importance of Control Performance(Emerson)


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Audits

Audits are the first step to ensure control system

investments meet expectations(Feb. 1999 CONTROL ENGINEERING Journal, by Dave Harrold)

Shown are the defects per loop e.g.

Filter Time Constant

Integral Period Derivative Period

Sampling Period

Proportional Band

Current Operating Mode

0%

16%

7%

4%

0%

34%

39%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

0 1 2 3 4 5 6

Number of Defects per Loop

Frequency


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XPS PC GrpCapabilities &Services

XPS PC Group www.myxps.ca

1. Process Design & Commissioning

2. Controls Auditing

3. Control Loop Optimisation

4. Advanced Controls

5. Slow Process Response

6. Off-Gas System Controls

7. Grinding Controls


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Process Control Will Not CorrectInherent Design / Flowsheet Problems

(McKee, AMIRA P9L)

There is a need to determine, and if necessary correct, thecondition of the plant as a pre-requisite to controldevelopment. A good example is the importance of classifieroperation and its effect on comminution circuit performance.

Techniques exist (plant sampling, modelling and simulation)to audit the actual plant operation. Correcting plantlimitations should be seen as a first step in the control

approach.


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XPS : Process Support Groups

Process Control - Identify and deliver robustprocess

control technology and engineering solutions to achieveOperational Performance Excellence.

Process Mineralogy - Design, implement and optimizemineral processing flowsheets by matching the flowsheetto the mineralogy.

Extractive Metallurgy Provide specialized extractivemetallurgy services (hydro-and pyro-metallurgical).

Flowsheet/project development using modeling andpiloting, new process development and plant optimization.

Materials Technology - Improve the reliability ofcritical equipment through appropriate implementation

of well proven materials engineering practices atessential stages of design, procurement and operation.


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XPS Process Mineralogy

PROCESS

MINERALOGY

Sampling and Statistics

MineralP

roce

ssing Mi

neralScience


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XPS Process MineralogyCrushing and Blending Plant

Staged crushing and screening prevents overproduction of fines

Drill core or ROM 150mm rock at 150 kg/hr

Blending technology produces RSD of < 5% in test charges

Any product size down to 1.7 mm split into any unit mass

Semi-continuous operation


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XPS Process MineralogyMineral Science

Developing Flowsheets that Work the first time

Modern Quantitative Mineralogy

Strategic

Virtual Flowsheeting/Flowsheet Implications


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XPS Process MineralogyMineral Processing

Developing Flowsheets that Work the first time

High-Confidence Flotation Testing*

Established 1995 95% confidence level Tried and tested Reproducible results Reliable scale-up

* Lotter, N.O., 1995, A Quality Control Model for the Development of High-Confidence Flotation Test Data,M.Sc. Thesis, University of Cape Town, June 1995


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XPS Process MineralogyMineral Processing

- Commissioned 2005

- 11 campaigns to date

- Reproducible results- Proven ability to mimic

operations

Mini-Pilot Plant

Demonstrating theOptimised Flowsheet


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XPS Process Mineralogy Montcalm Project

Flowsheeting fromDrill-Core to PilotPlant

Finds optimumflowsheet, or findsbest performanceattainable withknown flowsheet

Montcalm Project- Type 1 Startup*

Comparison of Montcalm Start-up Curve with McNulty Curves

0

20

40

60

80

100

120

0 2 4 6 8 10 12 14

Quarter after start-up

NioutputinNia

ndCuconc,%o

fdesign

Type 4

Type 3

Type 2

Type 1Montcalm start-up

Montcalm start-up - October 2004

*Type 1 reaches design capacity ~4 quarters


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Good Process ControlImplementation

(McKee, AMIRA P9L)

A good implementation requires a well definedoperating strategy and an associated control strategy.


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Effective Process Control Cycle

(for New Processes / Plants)

Process Opt.(Production)

Falconbridge Limited - Process Control

poor controlbettercontrol

higher production

lower costs

setpoint

constraint

best control

time

units

Overall Process Control Objective

Control OptimiseMeasure

As-builts

(Commissioning)

P&IDs

(Basic Eng)

Control Config.

(Construction-EPCM)

Logic Diagrams

(Detailed Eng-EPCM)

Process Flowsheets &

Control Philosophy(Development)

Deliverable

(Stage)


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Overall Process Control Objective

units

Control Optimize

poor controlbettercontrol

higher production

lower costs

setpoint

Process constraint

best control

time

Measure


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Poor to Optimized Control

Best Practise:

Necessary for:OperationalPerformanceExcellence


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5 Essential Control Loop Elements

& Consider the Whole Loop (ABB)

ABBABB Corporate ResearchK. Forsman, 1998, No. 10

Consider the whole loop

PC-program in

Advant OCS

D/A

I/P

Positioner

FT

A/D

Deadband

Filter

Ramp rate Filter

Actuator

Filter

3-15 psi

0-6 bar

4-20 mA

4-20 mA

mV

Five Essential*

Control Loop Elements:

1. Sensing element2. Transmitter3. Controller

4. Final Control Element (e.g.Actuator or VSD)5. Process

Only when all five elementsare performing their best will

the control system meetexpectations!

* February 1999 CONTROL ENGINEERINGby Dave Harrold.


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Regulatory Control improvements(AG Mill Bearing Pressure measurement)

Careful:

1) Appropriate filtering;

2) PI Data compaction.


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Mill Feed -

Manual Setpoint Control

Opportunity in optimising feed tonnage has been estimated at $8.8m/yr!


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General Process Control Hierarchy

Field / Panel / DCS / PLC

Instrumentation - Inputs / Outputs

Advanced

Regulatory

Manual

PlantOptimization

Optimize

Stabilize

FunctionObjective

Processes

Optimizing Control

Process

Plant

Optimization

Cash Optimization

EconomicsSite

Loop Control

Measure

EconomicReturn


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Era ofEra of ObjectivesObjectives ControlControlTechnologyTechnology

Quantity Tonnage ofConcentrate

PLC or

DCS

QualityTonnage + Quality

Client Satisfaction

Tonnage + Quality+

Peak EconomicPerformance

PeakPerformance

OptimizingControl Systems

OCS

Metso minerals - Jan. 2004 Technology Presentation

The evolution of

control can besummarized in thisslide.

The first shift wasfrom quantity onlyto quantity and

quality, bothapplied in a DCSor PLC.

However, to shiftfrom maintainingquality to peakperformancerequiressomething morethan a DCS orPLC: an optimizingcontrol system.


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Enabling Technologies - ABB

(Overall Process Unit Control)

MPC (Model Based)

Control:

Several tools are available:

Mintek (FloatStar) Emerson (DeltaV MPC)

ABB (Linkman, Expert Optimizer)

Invensys (Connoisseur)

Honeywell (Profit Suite)

Gensym (G2)

Prediktor

Metso (Adaptive Predictive Model)

Production cost

Lower

FeedbackHigher

Lower

Higher

Feedforward &Cascade

Cross-Coupled

Multi VariableTechniques

Knowledge

BasedExpert Control


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Model Predictive Controller

Constraint Management(LR or QP methods)

CVs, MVs & DVs

Optimal Setpoint& MV Target

LP Optimizer

Connoisseur Environment

Connoisseur (Invensys) Overview

Regulatory Control System

Neural Net

Adaptive Cont

C P

M

Fuzzy Logic

Director Calc

10*349304

1454

LD

dxSj

++

Non-linear

Inferential


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Elements Necessary for Successful

Process Control in Mineral Plants(P. Thwaites, IFAC MMM07, Plenary Address, Quebec City)

ProcessControl ->

OperationalPerformance

Excellence

Tools:Instruments;

Systems etc.

People:Control / Process

Knowledge

Successes:Results &

Examples

Actions:

Support

Management;

Technology Transfer


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Enabling Technologies

(McKee, 1999). In grinding, control of AG/SAG mill circuits is the dominantarea. While some systems have emerged which provide a reasonable level ofcontrol, there is still much not understood about the dynamic behaviour ofthese mills, and there is considerable scope for further development.

1. Multivariable Controller in a PLC Function Block (Bartsch):

SAG feed control, is generally done using an Expert system.

These systems often deliver improved control and 4 to 5%increased throughput (e.g. Collahuasi and Raglan Mills);

XPS has recently implemented a complex controller in a

(Concept) control block, negating the costs of an auxiliaryExpert System, and training / support of this system.


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Entire Grinding Controls Focus:

(Liberation efficiency and throughput)

150 mt

Surge BinSAG Mill

24 diameter

(2240 kW)

Metso Double Deck

Vibrating Screen (8 x 16)

1- 8 x 40 mm

2- 5 x 4 mm, 3 x 8 mm

Sandvik Cone Crusher

Hydrocone H4800

(250 kW)

6 x 15 Krebs GMAX

Cyclones

Vort: 4.5, Apx: 3

Ball Mill

14 dia. x 21

(2240 kW)

Flotation

Underground

Storage Bins

A B

11-CV-03

21-CV-01

21-CV-04

21-CV-0521-CV-06

21-CV-02

Cyclone Feed Density Control

MICFI

DI

DI

LI

PI

DIC

DIC

RSP

PIC

LICSelectLogic

SY

OUT

OUT

OUT

OUT

ADJUST FEED SPOR

CYC FEEDDENSITYSP

H/LLim

H/LLim

Ref: FTC Report Raglan: CycloneDensity Control,E.Bartsch, 11 November 2005

Cyc O/F Density byP/Box water addition

Cyc O/F Density byP/Box water addition

Cyc Feed Density by

trimming O/F density SPCyc Feed Density by

trimming O/F density SP

CONSTRAIN circulatingload by trimming Cyc

Feed Density SP

FFE Impact Meter

Wipfrag

(Size analysis) ASRi


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SAG Feed Rate Control (PID)

SAG Control - December 27, 2005

100

110

120

130

140

150

160

170

180

04:48:00 09:36:00 14:24:00 19:12:00 00:00:00 04:48:00 09:36:00

Time

Tonnag

e-mtph

4300

4350

4400

4450

4500

SAG

KPA

Tonnage

kPa

Setpoint = 4450 kPa

SAG Feed SAG Load

mpth kPa

Average 131.3 4 454

Std Dev. 9.7 24Minimum 100.5 4 392

Maximum 152.7 4 503

In Cascade control (constant kPa),

The SAG tonnage varies tremendouslyto maintain the requested setpoint.


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Why Online Feed Size Distribution?

Plot-0

TONNAGE ALIM.BROYEUR AUTOG

PUISSANCE BROYEUR AUTOGENE

PRESS. D'HUILE PALIER DECH

Alimentation Moulin AG D75

9/29/2002 9:30:00 AM 9/29/2002 9:30:00 PM12.00 Hour(s)

PRI-21WIC0204.SP

Mtph

PRI-21ML01.AV

kW

PRI-21PIT0457C.AV

kPa

PRI-21VT28.D75.AVG

m

20

40

60

80

100

120

140

160

180

0

200

1600

2500

0

4500

0.035

0.085

130.00000

2163.43750

4363.49463

0.04566

PRI-21WIC0204.SP

Mtph

PRI-21ML01.AV

kW

PRI-21PIT0457C.AV

kPa

PRI-21VT28.D75.AVG

m

Bearing Pressure

Mill PowerMill Feed Set-point

D75 Size Fraction

Large size

fraction (D75)INCREASING

Mill Load &Power

INCREASING

Large sizefraction (D75)

DECREASING

Mill Load &

PowerDECREASING


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SAG Charge Multivariable FuzzyController (Bartsch, CMP 2007)

Inputs

(Measurements)

Outputs

(Set-points)

Fuzzyfication

Fuzzy Rules

De-fuzzification

Power

Charge

Impactmeter

Granulometry(prediction !)

Assays

Feed Rate

Density

(water addn)Crusher Gap

Mill Speed

Programmed inexisting plant PLCs

ASRi (Automatic Setpoint Regulation)


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Crusher Gap Control:

Kidd Mill -> Strathcona Mill -> Raglan Mill

Field Device

DCSDisplay

(via OPC)

ASRi (Automatic Setpoint Regulation)

Sandvik Technology - Crushing Plants


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Mill Objectives *:

1. Increase the fineness of the grind to P80 = 752. Reduce Cyclone Feed Density to improve

classification efficiency.

* Ref: Report Raglan Optimization Project : Raglan Site Visit L Urbanowski, Jan 14 2006


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Typical Control Strategy:

LI LIC

SY

DIC

DI

Water addition monitored /

controlled to the pumpbox?:


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Cyclone Feed Density Control..or Cyclone Overflow Density Control?

Deficiencies in existing Strategy:Controlling cyclone feed density directlyby water addition not

considered best practice.

1. The feed density is slow to respond which means the loopcannot be tuned for load disturbance

2. The influence of adding water changes depending on your millcapacity:A. spare grinding capacity it will trim the feed density as

expected.B. at or over capacity -; it will increase circulating load and

density, resulting in more water addition .


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Requirements of a Density Control Strategy

1. Control water addition by using the moreresponsiveprocess variable (overflowdensity).

2. Consider the circulation load to avoidoverloading the ball mill.

3. Maximize circulating load in order to maximisemill efficiency.

4. Filter measured variables appropriately (i.e.match the process response times)

Ref: FTC Report Raglan: Cyclone Density Control, E. Bartsch, 11 November 2005


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Cyclone Feed Density Control(Best Practice)

MICFI

DI

DI

LI

PI

DIC

DIC

RSP

PIC

LICSelectLogic

SY

OUT

OUT

OUT

OUT

ADJUST FEED SPOR

CYC FEED DENSITY SP

H/LLim

H/LLim

Ref: FTC Report Raglan: Cyclone Density Control, E. Bartsch, 11 November 2005

1. Control water byusing the moreresponsive process

variable (CycloneOverflow Density).

2. Use the output ofthe Cyclone FeedDensitycontrolleras the remote(ext.) set-point forCyclone OverflowDensitycontroller.(This cascade is moresuited to the slowdynamics of the loop.)

3. Use a CirculatingLoadcontroller toeither trim FeedRate or CycloneFeed Densityset-point. N.B. feed willneed adjusting ifcirculating load doesnot reach steady state.

4. Filter appropriately

1

2

3


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Raglan Cyclone Feed Density ControlRef: FTC (XPS) Report Raglan: Cyclone Density Control, E. Bartsch, 11 November 2005

0

100

200

300

400

500

600

700

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77

February - March

40.0

50.0

60.0

70.0

80.0

90.0

100.0

110.0

120.0

130.0

Circ Load

Circ Load SP

D80

Reduced* p80 by 10 microns = 0.6% Ni rec increase worth approx. $2,000,000 pa.

(* ref feasibility study benchmarking 2004)

P80 80microns

P80 68microns

P80 90microns

%Circ

Load

Controllerplaced in

service


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Oscillating on/off Cyclone Switching

=> Oscillations in Cyclone AND Process Feed rates

- Cyclones (often) operating below their design pressure (40-60 instead of 100 kPa):

Increased short-circuiting of water & undersized particles;

IsaMill feed density decreases below design value;

Low IsaMill feed density may create pumping issues (& trip on low flow/pressure);

- Cyclone pressure should be controlled using the cyclone feed-rate.- Implement Surge Tank Control (using 150 m3 of tank) NOT Tight Level Control.


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Strathcona Mill Regrind Cyclone Overflow DensityResults - 3 days operation (including startup)

Plot-0

BREG CYCPRESSURE BREG CYCO/FDENSITY

1/18/02 7:16:18 PM 1/21/02 7:16:18 PM3.00Day(s)

CGCYCRBmv~C_PIC_2057:B_PID2057.MEAS

kPa

CGCYCRBmv~C_DIC_2060:B_AIN2060_1.PNT

% solids

50

100

150

200

250

300

0

350

20

50

192.74

29.0277


kPa


% solids

Plot-0

B REGCYC PRESSURE B REGCYC O/F DENSITY

3/21/02 7:16:18 PM 3/24/02 7:16:18 PM3.00 Day(s)


kPa


% solids

50

100

150

200

250

300

0

350

20

50

148.07

38.3157


kPa


% solids

Improved startup

CycloneFeedPressure

Cyclone

Overflowdensity

Overflow Density:

9% density

improvement

65% reduction instandard deviation



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Excellent practise is an OPPORTUNITY in our Canadian Mills!

2. On-Demand Sampling Automation:


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3. Camera Imaging, flotationlevel & reagent controls

- Feed size analysis;

- Froth camera imaging;

- Optical System forcathode quality:

39

Froth Camera Imaging Technology

CSQA:Cathode SurfaceQuality Analyzer(Aplik)

Fl t t th T E i


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Float to the Top Energise your

Flotation PerformanceAuthors: A Okely, A Rinne, A Peltola (Outotec)

The relevant cost factors for a flotation plant are investment, energy, reagentconsumption, and maintenance.

The chart below shows the breakdown of these factors, based on typical ownershipcosts of a large mechanical flotation machine (100-200 m3) over a 25 yearlifespan.

If we look at the energy

efficiency we find that threeaspects are critical:

1. Air dispersion2. Rotational speed of the

mechanism3. Component wear

Optimal air dispersion is one of the basic requirements for goodmetallurgical performance. Plants operating with forced air cells have

often noticed that the best results are achieved using individual andvarying air feed rate in each cell.

Canty Vision Froth Camera


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Canty Vision Froth Camera

(Strathcona Mill)


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Flotation Level Controls

Level Control:

Absolute basics for Flotation.

The movement of the pulp level setpoint provides opportunities forflotation optimization if there is tight control in each cell. (Ref., PT, 1983)

R h L l Di b


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Rougher Level Disturbance

Setpoint

Level

FrothVelocity

1. Velocity (mass pull)changes by over1000%!

2. Baseline pull isonly 0.5 1 cm/s! :Level SP too low?

Escondida Froth Velocity is Cascaded to


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yControl Flotation Cell

(metso minerals & Ramon A. Brito Minera Escondida)

SP AireSP Velocidad

SP Nivel

PV Velocidad

Tapon

Celda WEMCO

Camara

Escondida: Instalacin en la FlotacinPrimaria Lneas 1,2,3,4,5,6

(54 Cameras)

Rougher flotation


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Rougher flotationTest Control strategy step1


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Froth velocity control

Feed

grade

Froth

veloc.

Level

SP

Air SP

Tails

%Ni

Flotation Control: Points on Control


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Structure and importance of layering(Gilles)

Flow Control

MetallurgicalControl

Ratio Control

Set Point:

cc/min

Measurement:Tonnage orTonnage*Head Grade

Set Point:Gram/Ton

Measurement:(Tail or Conc

Assay)

Set Point:Operator Recovery orGrade Target

All of the Met. Loops are PI/PID controllers for supportability.

If problems occur, operators may turn off one level of control at a time, independently for

each reagent.

Addendum:


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Addendum:Thickener Controls

For effective Thickener control 3 measurements are important:

1. Inventory (Inferred from pressure measurement in the Cone) controls underflow pump speed.2. Bed level

controls flocculent addition rate.3. Overflow clarity

indication/warning of poor control.

None of the above measurementsare trivial and require attention toequipment selection and installation.

Typical Bed Mass Level Measurement on aThi k


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Thickener

Shown is anE&H Deltabar S

smart (2 wire)differentialpressuretransmitter.

It is ideally used to

measure level,pressure ordifferential pressure.

Uses a flushmounted ceramicdiaphragm sincethey out last themetallic diaphragmsby at least 10 years.

(Ref. M. Gribbons)



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4. Flow Measurement:

SONARSONAR A New Class of Meters with Distinct Application AdvantagesA New Class of Meters with Distinct Application Advantages

SONAR

Gas Void Fraction GVF-100

ENTRAINED AIR Meters

Gas Holdup MeterGH-100

Volumetric Flow Meter

Ultrasonic

Coriolis

Vortex

MultivariableDP

Magmeter

Ref: Christian [email protected]

SONARtrac Enables Accurate MassSONARtrac Enables Accurate Mass


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SONARtrac Enables Accurate MassSONARtrac Enables Accurate Mass

Balance in the Presence of AirBalance in the Presence of Air

Application:Hydrocyclone Feed Line

Challenge: Variable entrained air levels causing errors in density reading and hydrocyclone split This affects both flotation performance as well as ball mill circulating load

0

2 0 0 0

4 0 0 0

6 0 0 0

8 0 0 0

1 0 0 0 0

1 2 0 0 0

1 4 0 0 0

1 6 0 0 0

1 8 0 0 0

1 /2 5 /2 0 0 6 4:4 8 :0 0 1 /2 5 /2 0 0 6 6:0 0 :0 0 1 /2 5 /20 0 6 7 :1 2 :0 0 1 /25 /20 0 6 8 :2 4 :0 0 1 /25 /2 00 6 9 :3 6 :0 0

T im e

Volumetr

icFlow(gpm)

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

5.0%

GasVolu

mneFraction(%)

V F ( g p m )

G as Volum e Frac t ion

Flow Rate

Entrained Air %



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5. Rotating Equipment Machinery Health:



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6. Wireless Instrumentation:

GITE-322-05-PRE-15

HighServiceINDUSTRIAL SUPPORT COMPANY

2.- Proposed Solution

Electronic Wear Sensor (EWS) embedded in liners andlifters fastening bolts

GITE / DES

Main components of the EWS: Transducer

8-12 levels

CPU (coder & signal processing)statistical filtering

RF Transmitter,FM FSK, 916 MHz, 1mW,

anti-collision algorithmSensor Life: 18 month

Business Case 3:Smart Wear Sensor

Rev0 Fecha 100807 Pgina 19 de 29

C St d E l


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Case Study Example

CBU: Xstrata Nickel (Sudbury operations)Site: Strathcona Mill

Project: Primary Grinding ControlStatus: Completed in 2008Comments:

Maintain Consistent Grind to Flotation Adjust, & optimise Rod Mill Feed

Automatically to Capacity of GrindingCircuit Eliminate Process Upsets due to Ore

Variability (i.e. Mill Overloads) Reduced energy consumption by 7.1% &

7.5% in the rod & ball mills respectively

Implement on 2 grinding lines Presented at the Jan. 2009 meeting of the

Canadian Mineral Processors.8.48.17.87.57.26.96.6

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

BM kW/t

Density

7.773 0.2732

7.189 0.2862

Mean StDev

Old Ctrol

New Ctrol

Status

Histogram of BM kW/tNormal

3.363.243.123.002.882.762.64

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

RM kW/t

Density

3.123 0.1275

2.900 0.1168

Mean StDev

Old Ctrol

New Ctrol

Status

Histogram of RM kW/tNormal

Rod Mill (Power/tonne):

Ball Mill (Power/tonne):


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Strathcona Mill Grinding Circuit

McIvor and Finch, 1991: Besides achieving the desired mineral d80size, it is clearly desirable to produce as narrow a size distribution as

possible to squeeze the maximum amount of the mineral value intothe highest recovery region.

Inadequate mineral liberation in itself leads to higher energyconsumptions, as finer grinding has to be performed for liberation.

24521017514010570350

0.16

0.12

0.08

0.04

0.00

RMF (Tons/hr)

Density

173.3 33.95 719

188.2 2.866 721

MeanS tDev N

1Before

2After

Status

6057545148454239

0.8

0.6

0.4

0.2

0.0

COFD (%Solids)

D

ensity

49.81 4.844 704

47.14 0.4740 721

Mean StDev N

1Before

2After

Status

Histogram of RMF (Tons/hr)Normal

Histogram of COFD (%Solids)Normal

Strathcona Grinding Circuit

Process ModelingProcess ModelingProcess ModelingProcess Modeling

0.5

0.6

0.7

0.8Step Response

PBLs730 10


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Strathcona Grinding Circuit

Process Modeling:

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 1000040

50

60

70

80

y1

Input and output signals

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000140

150

160

170

180

Time

u1

PumpPumpPumpPump----boxboxboxbox

level (PBL)level (PBL)level (PBL)level (PBL)

Rod millRod millRod millRod mill

feedfeedfeedfeed

((((RMFspRMFspRMFspRMFsp))))

0 500 1000 1500 2000 2500 3000 350043

44

45

46

47

48

49

y1

Input and output signals

0 500 1000 1500 2000 2500 3000 3500115

120

125

130

135

140

145

Time

u1

CycloneCycloneCycloneCyclone

OF densityOF densityOF densityOF density

(COFD)(COFD)(COFD)(COFD)

PumpPumpPumpPump----boxboxboxbox

waterwaterwaterwater

((((PBWspPBWspPBWspPBWsp))))

0 5000 10000 15000-15

-10

-5

0

5

10

15

Time

Measured and simulated model output

0 500 1000 1500 2000 2500 3000 3500 4000-3

-2

-1

0

1

2

3

Time

Measured and simulated model output

COF Particle Size and Pulp Density

Correlation between COF Particle Size and Pulp Density

72.00

74.00

76.00

78.00

80.00

82.00

84.00

86.00

35.00 40.00 45.00 50.00 55.00

COF Density (% solids)

COFP.S

ize(%-150m

esh)

Data from Strathcona Mill November 2007 Grinding surveys

Two SISO, simple PI controllers:1. Cyclone Over Flow Density (COFD) by

manipulating the Pump-Box Water(PBWsp)

2. Rod Mill Feed (RMFsp) based on the

Pump-Box Level (PBLsp)

Grinding Control Objective:

Maximize the throughput (quantitativeobj.) while maintaining

the cyclone OF density at target(qualitative obj.)

11

0 100 200 300 400 5 00 600 7 00 8 00 900 1 00 0-0.2

-0.18

-0.16

-0.14

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

Time

Step Response

0 500 1000 1500 2000 2500 30000

0.1

0.2

0.3

0.4

Time

s

e

RMFsp

PBLs

4851

73.0 10

+

=

s

e

PBWsp

COFD s

1251

18.0 15

+

=


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Old Control Strategy


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New Control Strategy

B f Old C t l


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Before - Old Control

OLD CONTROL

PBL manipulating PBWPBL manipulating PBWPBL manipulating PBWPBL manipulating PBW

Some control, but not process control

Fixed tonnage and oscillating cyclone overflow density

Mill feed shut down intermittently to handle mill overloads Upsetting all downstream flotation processes

Increase of milling rate can take hours

After New Control


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After New Control

11

NEW CONTROLNEW CONTROLNEW CONTROLNEW CONTROL1. COFD manipulating PBW and1. COFD manipulating PBW and1. COFD manipulating PBW and1. COFD manipulating PBW and

2.RMF based on PBL2.RMF based on PBL2.RMF based on PBL2.RMF based on PBL

Process control

Controlled cyclone overflow density (particle size) and maximisedrod mill feed rate

No more mill feed shutdowns controller prevents mill overloads

No more grindouts

Controller reacts quickly to changes in ore hardness

Easier to operate, consistently for each shift

Key Results

200

180

160

55

45

35S

PT(t/h)

RMWi

RMWi

RMF SPT (t/h)

V ariable

Time Series Plot of RMWi, RMF SPT (t/h)


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Key Results(Over a Longer Period)

Period: (Jan.07-May.08 vs Jun.08-Sept.08)

Increase in circuitthroughput of7.7%:

(168 to 181 tph); fully realised upon treatment

of Ni Rim South ore;

Grinding circuit feed toflotation (COFD) maintainedat target density: reduction in variability from

2.0 to 0.8;

An increase in energy efficiency(kW/t) of:

7.1% for the rod mill and

7.5% for the PBM;

No Mill overloads;

No degradation in Ni or Cu

Recoveries.

09/24/0806/13/0804/18/0802/25/0812/09/0710/02/0707/29/0706/08/0704/14/0703/01/0701/10/07

140

120

25

15

Date

RMFS R

198192186180174168162156

0.060

0.045

0.030

0.015

0.000

RMF SPT (t/h)

Density 168.2 5.756 517

181.2 7.098 77

Mean StDev N

1Before

2After

Status

Histogram of RMF SPT (t/h)Normal

09/24/0806/13/0804/18/0802/25/0812/09/0710/02/0707/29/0706/08/0704/14/0703/01/0701/10/07

60

50

40

30

Date

COFD(%)

525048464442

0.48

0.36

0.24

0.12

0.00

COFD (%)

Density 46.61 2.008 517

46.22 0.7656 77

Mea n S tDev N

1Before

2After

Status

Time Series Plot of COFD (%)

Histogram of COFD (%)Normal


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Feedback CommentsOperators:

This has made the operations of the mills that much

easier and efficient!DD

It is working great and it is helping flotation as well. RA

This is better and the right way to do it. SH

To do better than the controller, I have to take samplesevery 15 minutes. BR

I thought this will not work, but it is working very well.JM

Phil Thwaites (Manager Process Control):

Xstrata operates many grinding circuits. I believe thatbenefits such as these (as also demonstrated at RaglanMill & Kidd Mill) can be duplicated at several other plantswith similar grinding circuits.We often see very poor cyclone and grinding circuitcontrols that have not been tuned or optimised.In this project we have found the best way to control

these two circuits the sweet spots (after all these yearsof operation).

Perceptive Engineerings Focus:


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70

p g g

(Vision - Sandoz)

Optimisation

& Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent& Soft Sensors

Conventional Sensors

& Instrumentation

Operating Constraints

Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 120 140 160 1800

5

10

15

20

25

30

35

40

Time

SPE

SPEConfidence Limit

Early Warning Process

Condition Monitors

Classification for

Quality Control0 20 40 60 80 100 120

10

15

20

25

30

35

40

Sample No.

FeO

Soft Sensors

Integrated Condition Monitoring

and Advanced Process Control

Control System

Integration


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Concluding Remarks

Plant automation is often seen as the project deliverable,when what is really required is plant control.

There are many approaches, instrumentation, and multiplecontrol systems, together with numerous advanced controlpackages to select from.

Process control is more than just tools. Successful plant implementation is reliant on these together

with: process knowledge, a solid control engineering background /experience, and

the operations team willing to act / implement / support theimplementations.

Together, robust solutions can be realised, minimising

process variation and optimising process performance. This will result in an easier, efficient and safer process to operate.

(P. Thwaites, AUTOMINING2008, Chile)


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At previous (high) metal prices the results from good

control, and Operation Performance Excellence are

substantial!

At current metal prices good control and Operational

Performance Excellence is essential!

Operational Performance Excellence requires a solid

performance of the regulatory layer AND process optimisation.

Organizational structure and human resources are important

in achieving Operational Performance Excellence.

Thank you.

desarrollo de control de procesos en strata cooper

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